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Background: Ward rounds are an important and ubiquitous element of hospital care with a history extending well over a century. Although originally intended as a means of educating medical trainees and junior doctors, over time they have become focused on supporting clinical practice. Surprisingly, given their ubiquity and importance, they are under-researched and inadequately understood. This study aims to contribute knowledge in human reasoning within medical teams, meeting a pressing need for research concerning the reasoning occurring in rounds., Methods: The research reported here aimed to improve the understanding of ward round reasoning by conducting a critical realist case study exploring the collaborative group reasoning mechanisms in the ward rounds of two hospitals in Victoria, Australia. The data collection involved observing rounds, interviewing medical practitioners and holding focus group meetings., Results: Nine group reasoning mechanisms concerning sharing, agreeing and recording information in the categories of information accumulation, sense-making and decision-making were identified, together forming a program theory of ward round reasoning. In addition, themes spanning across mechanisms were identified, further explaining ward round reasoning and suggesting avenues for future exploration. Themes included the use of various criteria, tensions involving mechanisms, time factors, medical roles and hierarchies., Conclusions: This paper contributes to the literature by representing rounds in a manner that strengthens understanding of the form of the group reasoning occurring within, thus supporting theory-based evaluation strategies, redesigned practices and training enhancements.

This paper describes a narrative-based interactive learning environment which aims to elucidate reasoning using interactive scenarios that may be used in training novices in decision-making. Its design is based on an approach to generating narrative from knowledge that has been modelled in specific decision/reasoning domains. The approach uses a narrative model that is guided partially by inference and contextual information contained in the particular knowledge representation used, the generic/actual argument model of structured reasoning. The approach is described with examples in the area of critical care nursing training. A study of the effectiveness of this approach on learning outcomes was conducted with final year nursing students and provides evidence of improved learning outcomes. (Contains 4 tables and 7 figures.)

In this paper we present the notion of structured reasoning through a model, called the Generic/Actual Argument Model (GAAM). The model which has been used as a computational representation for machine modelling of reasoning and for hybrid combinations of human and machine reasoning can be used as a coalescent framework for decision making. Whilst the notion of structuring reasoning is not new, structured reasoning is advanced as a technique where group consensus on reasoning structures at various levels can be used to facilitate the comprehension of complex reasoning particularly where there are multiple perspectives. For an issue, the approach provides a scaffolding structure for cognitive co-operation and a normative reasoning structure against which group participants can identify points of difference and points in common as well as the nature of the differences and similarities. Intra-group transparency characterized by the ability to recognise points in common and understand the nature of differences is important to the process of coalescing group decisions that carry maximum group support. [ABSTRACT FROM AUTHOR]

This paper describes a narrative-based interactive learning environment which aims to elucidate reasoning using interactive scenarios that may be used in training novices in decision-making. Its design is based on an approach to generating narrative from knowledge that has been modelled in specific decision/reasoning domains. The approach uses a narrative model that is guided partially by inference and contextual information contained in the particular knowledge representation used, the generic/actual argument model of structured reasoning. The approach is described with examples in the area of critical care nursing training. A study of the effectiveness of this approach on learning outcomes was conducted with final year nursing students and provides evidence of improved learning outcomes. [ABSTRACT FROM AUTHOR]

Narrative and story telling has a long history of use in structuring, organising and communicating human experience. This paper describes a narrative based interactive intelligent learning environment which aims to elucidate practical reasoning using interactive emergent narratives that can be used in training novices in decision making. Its design is based on an approach to generating narrative from knowledge that has been modelled in specific decision/reasoning domains. The approach uses a narrative model that is guided partially by inference and contextual information contained in the particular knowledge representation used, the Generic/Actual argument model of structured reasoning. The approach is described with examples in the area of critical care nursing training and positive learning outcomes are reported. [ABSTRACT FROM AUTHOR]

This article describes an argumentation shell to support the formulation, representation and drafting of legal arguments. The shell can be used to capture generic arguments in many legal domains as well as to assist decision-makers in constructing their own actual arguments. The shell demonstrates that knowledge represented using the generic/actual argument model (GAAM) (a variant of Toulmin's argument structure) can be used to: (a) support the development of complex arguments, (b) add context and increase specificity for the retrieval of relevant documents, (c) incorporate background knowledge, (d) assist in the drafting of documents that represent arguments made, and (e) provide a structure for complex inferences requiring a range of mechanisms. The shell can be used to support decision making in a range of legal domains, including discretionary domains. [ABSTRACT FROM AUTHOR]

Background: Regression-based research has successfully identified independent predictors of smoking cessation, both its initiation and maintenance. However, it is unclear how these various independent predictors interact with each other and conjointly influence smoking behaviour. As a proof-of-concept, this study used decision tree analysis (DTA) to identify the characteristics of smoker subgroups with high versus low smoking cessation initiation probability based on the conjoint effects of four predictor variables, and determine any variations by socio-economic status (SES).Methods: Data come from the Australian arm of the ITC project, a longitudinal cohort study of adult smokers followed up approximately annually. Reported wanting to quit smoking, worries about smoking negative health impact, quitting self-efficacy and quit intentions assessed in 2005 were used as predictors and reported quit attempts at the 2006 follow-up survey were used as the outcome for the initial model calibration and validation analyses (n = 1475), and further cross-validated using the 2012-2013 data (n = 787).Results: DTA revealed that while all four predictor variables conjointly contributed to the identification of subgroups with high versus low smoking cessation initiation probability, quit intention was the most important predictor common across all SES strata. The relative importance of the other predictors showed differences by SES.Conclusions: Modifiable characteristics of smoker subgroups associated with making a quit attempt and any variations by SES can be successfully identified using a decision tree analysis approach, to provide insights as to who might benefit from targeted intervention, thus, underscoring the value of this approach to complement the conventional regression-based approach. [ABSTRACT FROM AUTHOR]